EP3702202A1 - Verfahren und system zur optimierung der effizienz und der benutzererfahrung einer verteilten ladestation - Google Patents

Verfahren und system zur optimierung der effizienz und der benutzererfahrung einer verteilten ladestation Download PDF

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Publication number
EP3702202A1
EP3702202A1 EP19159973.7A EP19159973A EP3702202A1 EP 3702202 A1 EP3702202 A1 EP 3702202A1 EP 19159973 A EP19159973 A EP 19159973A EP 3702202 A1 EP3702202 A1 EP 3702202A1
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Prior art keywords
charging station
charging
vehicle
vehicles
requesting
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Granted
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EP19159973.7A
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English (en)
French (fr)
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EP3702202B1 (de
Inventor
Jilei Tian
Yang Cao
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Bayerische Motoren Werke AG
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Bayerische Motoren Werke AG
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Priority to EP19159973.7A priority Critical patent/EP3702202B1/de
Priority to US17/418,466 priority patent/US20220076358A1/en
Priority to CN201980083524.2A priority patent/CN113195299B/zh
Priority to PCT/EP2019/078609 priority patent/WO2020173584A1/en
Publication of EP3702202A1 publication Critical patent/EP3702202A1/de
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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    • G01—MEASURING; TESTING
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    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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    • G—PHYSICS
    • G01—MEASURING; TESTING
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    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
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    • G06F17/12—Simultaneous equations, e.g. systems of linear equations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/60—Navigation input
    • B60L2240/68—Traffic data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/70—Interactions with external data bases, e.g. traffic centres
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B60L2260/54—Energy consumption estimation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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    • Y02T10/72—Electric energy management in electromobility
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
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    • Y04S30/14—Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Definitions

  • the invention relates to a method for optimized allocation of highly distributed charging stations to electrical vehicles to improve charging stations efficiency and to improve user experience, and to a computer-readable medium and a system for the same.
  • Another approach is aiming to improve the user experience by positioning charging station locations on an interactive map and allowing users to navigate to the charging stations based on the locations of the map.
  • the invention allows optimization of a total detour distance for charging a plurality of vehicle, in contrast to optimizing the charging detour distance for each vehicle separate.
  • the optimization on detour distance is an example and the invention may be optimizing any target, such as prices for charging or waiting time in queues, such as for vehicles queueing for charging.
  • the optimization may be on one of the targets.
  • the optimization may also be on more than one target simultaneously, such as by weighting targets.
  • the objective may also include several optimization targets, that preferable are weighted with respect to each other.
  • the optimization may also include one or more constraints as described herein.
  • Gasoline refueling services generally is dependent on investment in infrastructure in rather complex and dangerous equipment due to the gasoline supply management, such a pipelines and/or fuel delivery etc. This has resulted into branded services, e.g. Shell, BP, etc.
  • branded services e.g. Shell, BP, etc.
  • the invention includes realizing that individuals may in theory offer their charging station as a service.
  • the invention further includes realizing that this may lead to a trend shift in the distribution of charging stations services, in terms of geographical locations, and in terms of ownership.
  • Charging station services can be highly distributed.
  • the invention includes realizing the possibilities, and also the technical challenges this potential trend shift may lead to.
  • the invention also includes a addressing the technical challenges with a technical solution for optimizing charging station allocation in such highly distributed environment of charging services.
  • This invention proposes an approach to charging service as the whole system to optimize the performance and value between supplier and demand.
  • it is an object of the present invention, to optimally allocate charging stations to electrical vehicles.
  • the object of the present invention is solved by a computer-implemented method for allocating charging stations, the method comprising:
  • the requesting vehicle may be able to know feasible routes between departure location and arrival location. Determining detours data may be done based on feasible routes between these locations and charging station locations.
  • the advantages include, in particular for highly distributed charging stations, optimizing charging stations efficiency and user experience.
  • the advantages of the holistic approach and the allocation of charging stations include an improved utility of charging stations.
  • the objective may be to optimize across vehicles/users (demand) and service providers (supply). This may in turn lead to improved value for suppliers and consumers.
  • the invention may enable and motivate more charging facility for public use, as well as allow service providers to maximizing their profit with the system easy to manage, as well as meeting personal service availability.
  • the system allows reduced charging costs, and reduced waiting and detour distance over time.
  • the method may be used to recommend, and remind users of the best time and location for charging their vehicle, e.g. short detour, low price, no waiting time, optimal charging efficiency.
  • the invention has further advantages for transportation based on autonomous vehicles, where vehicles may be adapted drive and charge themselves without passengers. Total detour distance, and so cost of transportation, may be reduced by optimizing on a fleet of vehicles, rather than optimizing for each vehicle individually.
  • the advantages also include values for community and ecosystem such as environment, traffic, utility and energy efficiency.
  • the request for charging may be done manually by a user of an EV, or may be done automatically by the EV itself when it, based on sensor inputs, consider it needs charging.
  • the request may triggered centrally, such as in a central charging station allocation unit, the charging station allocation unit being updated (such as by pull or push messages) from EV with EV data needed for the charging station allocation unit to make a decision that an EV needs charging.
  • EV data may include current battery levels and/or distance to charging stations.
  • the charging station allocation unit may administer a charging station allocation schedule for one or more vehicles that may be updated as described herein based on data from vehicles.
  • Charging station candidates may be presented to users of vehicles and in response to the presentation, the user may acknowledge the candidate and reserve the charging station so that the charging station is allocated to the vehicle.
  • the allocation may also be automatic, for some or all vehicles, in response to identifying charging station candidates.
  • the optimizing of the objective function, and the allocation of charging stations may be performed each time a new vehicle requests charging. It is then possible that a user experience that an initial short detour for charging is progressively increased the closer in time the charging is scheduled to be, as more vehicles are added to the optimization. This may be perceived by consumers as unjust.
  • the optimization may be done at certain intervals, where already allocated charging stations are not updated as more vehicles are requesting charging. "Early birds" will then have more charging stations included in the optimization and higher likelihood of short detour distances. This might lead to a delay for users to be suggested charging station candidates and may therefore be better suited for automatic allocation of charging stations. For autonomous vehicles, and in particular when driving for charging without passenger, the re-allocation will be less problematic.
  • the charging station detour distance is a difference in distance between a first distance of a path going from the departure location to the arrival location via the charging station locations, and a second distance of a path going from the departure location directly to the arrival location.
  • the advantages include an accurate and fast method for deriving the detour distance that the allocation of charging stations may be optimized on.
  • the method further comprises the step of
  • a challenge with a system approach is the complexity of the optimization problem and the increased computational demands with increased number of requesting vehicle and charging stations.
  • the advantages include a reduced number of vehicles by only including vehicles that is in need of charging at any moment in time.
  • the determining if the vehicle needs charging may be automatic, and a charging station proposed to the driver, reducing the attention needed by the driver on the battery level of the vehicle, further increasing the focus on driving and increasing safety.
  • the vehicle may schedule a charging after the current driving route is completed, when the battery level is below a threshold level.
  • the departure location for calculating the detour may then be the destination of the current route, and the arrival location for calculating the detour may be the start (pick-up) location of the next route.
  • identifying charging station candidates includes determined if a charging station is a valid candidate for a vehicle, in particular one of the requesting vehicles, where the charging station is a valid candidate for the vehicle if:
  • the advantages include reducing the complexity of computations by reducing the number of vehicles candidates for each charging station. By pre-filter vehicles based on their location and battery level, the optimization problem may be substantially reduced in complexity. Further, by including a detour distance threshold the method prevents outliers, i.e. vehicles travelling far for the sake of reducing the overall detour distance. The method may be adapted so that autonomous empty vehicles have a higher detour distance threshold than vehicles with passengers.
  • the detour distance may be in absolute terms of detour distance, or in relative terms (such as a percentage) of the tour distance without charging.
  • At least one request identifies an earliest departure time
  • the linear constraints include the earliest departure time whereas a charging station candidate is identified and allocated for the vehicle of the request so that the vehicle of the request is scheduled to depart for charging after the earliest departure time; and/or wherein at least one request identifies an latest departure time, and the linear constraints include the latest departure time whereas a charging station candidate is identified and allocated for the vehicle of the request so that the vehicle of the request is scheduled to depart for charging before the latest departure time.
  • the advantages include a further constraint to allow charging station detours to be better planned in advance for charging during a planned future route.
  • the earliest departure time may be the estimated arrival time of the present tour, so that the autonomous vehicle may drop of traveling passengers, before initiate driving to an allocated charging station for charging.
  • the charging station data further identifies opening hours of at least one of the charging stations and the objective function and/or the linear constraints include the opening hours so that the objective function is optimized, and the vehicles are scheduled for charging during the opening hours.
  • the advantages include a further constraints to allow for charging station to be out of service during certain time period.
  • the constraint allow optimization of routes to take into consideration that charging station may be needed by the owner, be pre-booked, need maintenance, or needs to be closed for service or for other reasons.
  • identifying charging station candidates includes determining a time window for the requesting vehicles, and the linear constraints are adapted so that the requesting vehicles are exclusively allocated to a charging station during the time window; and wherein the method further may comprise: f) determine a departure time for each of the requesting vehicles so that the vehicles is estimated to arrive to the charging station before the start of the time window.
  • the advantages include that that a vehicle is signaled a departure time, so that the vehicle is estimated to arrive at the charging station in time for the allocated time window for charging. It prevents vehicles from departing to early, which may lead to waiting time at the charging station, and clogging of the charging station approach way. If the vehicle departures after the determined departure time, the system may be notified, and the vehicle may be re-allocated a new time window at the same chagrining station, or re-allocated another charging station that is available according to the method described herein.
  • the linear constraints include the constraint: s i , j ⁇ q k , j or s k , j ⁇ q i , j ⁇ j ⁇ C ⁇ i , k ⁇ U j i ⁇ k
  • the advantages of including the above linear constraints include that the allocation of charging stations ensures that every charging station is only allocated to one vehicle at any one point in time. If a location contains several charging stations, each charging station is optimized as a separate charging station.
  • the objective function and/or the linear constraints include a cost of charging each of the vehicles.
  • the cost of charging may be a constraint, such as a maximum cost set by a user, or included as a minimization target, to reduce the total cost of charging.
  • the optimization may be balanced with the total detour so that vehicles allocated a charging station with short detour (such as popular charging stations) are charged a higher price than vehicles allocated a charging station with longer detour (such as less popular charging stations). This may lead to an cost effective allocation of vehicles, while supplier increase value of service, and cost of charging and detour decreases, and may be used to provide suggestions for installation of further charging stations at locations which would have a high value of service, that in turn would lead to a decrease in total charging detour distance.
  • the requests further identifies current battery level and an energy consumption per distance unit for a requesting vehicle and at least one of the charging station data identifies a price per hour and/or price per energy unit; and wherein the cost of charging is derived from the current battery level, the energy consumption per distance unit, a distance to each of the charging stations, and the price per hour and/or price per energy unit identified by the charging station data.
  • the advantages include a service that allow the total cost of charging the vehicle to full at each charging station, taking into consideration the current battery level and energy consumption to drive to the charging station.
  • the charging price such as per hour usage, or per energy unit charged
  • the supplier can adjust the price so that total return on investment is optimized.
  • the system may suggest a suitable charging price as a service, or set the price directly, taking into consideration the demand to charge at the charging station.
  • identifying charging station candidates includes applying a branch and bound method to optimize the objective function with the linear constraints.
  • optimizing the objective function includes maximizing the function ⁇ i ⁇ U ⁇ j ⁇ C i e i , j ⁇ ⁇ 1 ⁇ i ⁇ U ⁇ j ⁇ C i e i , j ⁇ detour i , j ⁇ ⁇ 2 ⁇ i ⁇ U ⁇ j ⁇ C i e i , j ⁇ pc i , j
  • the advantages include a method that provides a solution that is optimal considering a trade-off between minimizing detour distance and minimizing charging costs.
  • the trade-off may be adjusted by the parameters ⁇ 1 and ⁇ 2 and may be predetermined or adjusted dynamically depending on conditions.
  • the linear constraints include: ⁇ s i , j ⁇ ts i + tsc i , j ⁇ e i , j ⁇ i ⁇ U , ⁇ j ⁇ C i s i , j ⁇ te i + tsc i , j ⁇ e i , j .
  • the advantages include an allocation of charging stations that ensures that vehicles are able to reach and complete charging within the operation hours of respective allocated charging station.
  • the object of the present invention is solved by a computer readable medium comprising instructions, when executed by a processor, cause the processor to carry out the method as described herein.
  • charging station allocation system comprising:
  • the advantages include that distributed charging stations are informed of their allocation to vehicles which, for instance, may allow the charging station to prepare for charging such as indicating that the charging station is allocated to a vehicle. This preparations may include filling short term energy buffers, and/or adjusting voltage/ampere and/or connectors to the vehicle to which it is allocated.
  • This benefits and advantages of the system are further equal or similar to the advantages of the method described herein.
  • the charging station is able to identified the particular requesting vehicle; and wherein the charging station allocation system prevents vehicles other than the particular requesting vehicle from using the charging station during the corresponding time windows.
  • the advantages include that the charging station is available, i.e. not occupied, when the vehicle to which it is allocated is arriving for charging. This prevents re-allocating the allocated vehicle and a suboptimal re-routing and/or waiting of vehicles to be charged.
  • Fig. 1 shows a first charging station location lc1, a second charging station location Ic2, a departure location for first vehicle ls1, departure location for second vehicle Is2, arrival location for first vehicle le1, arrival location for second vehicle le2, distance from departure location to arrival location for first vehicle dse1, distance from departure location to arrival location for second vehicle dse2, distance from Is1 to Ic1 (dsc1.1), distance from ls1 to lc2 (dsc1.2), distance from ls2 to Ic1 (dsc2.1), distance from ls2 to lc2 (dsc2.2), distance from Ic1 to le1 (dse1.1), distance from lc2 to le1 (dse1.2), distance from Ic1 to le2 (dse2.1), and distance from lc2 to le2 (dse2.2).
  • Same naming convention applies to other parameters, e.g. travel duration, time instance, departure and destination locations, etc.
  • the invention involves allocating charging stations (at location Ic1 and Ic2) to vehicles (with departure location Is1 and Is2) by optimizing an objective function, so that the total detour distance is minimized.
  • Fig. 1 involving two vehicles, and two charging stations, there are two possible solutions.
  • the number of vehicles and charging stations in Fig. 1 is an example, and the invention may process any number of vehicles and charging stations for allocation. The number of possible solutions, and so the complexity of the computations increases with the number of vehicles and charging stations processed.
  • the first letter of a notation mean: Location (I), Distance (d), Duration or time (t), Price (p), Batter level (B), Opening (o).
  • the notations for distance (d) and time (t) have generally tree letters where the second denotes starting point: starting location (s) or charging location (c), and the second denotes end point: charging location (c) or end location (e).
  • the notations for location (I) generally have two letters, where the second denotes location, such as start location (s), charging location (c), and end location (e).
  • the first one (i) generally refer to vehicle/user ID
  • the second (j) generally refer to charging station ID
  • Fig. 2 shows steps of a method according to one embodiment of the invention.
  • the method include a step of receiving charging station data 410, a step of receiving and/or determining one or more requests to charge a requesting vehicle 420, a step of receiving and/or determining detours data 430, a step of identifying charging station candidates 440, and a step of allocating charging stations to vehicles 450.
  • the method may also a step of determine departure time for the vehicles (not shown).
  • the step of identifying charging station candidate may include finding potential charging station candidates for the users who demand charge service and recommending users charge stations, including the optimized charging time, and that meet user's charging requests, while keeping the overall price and detour distance optimized using mixed integer programming.
  • Finding potential charging station candidates for the users who demand charge service may include (example):
  • Global optimization may include an objective of optimization to recommend each user to the best charge station and the best time user starts the charging
  • the following variables are introduced:
  • the objective may be to meet the most charging requests while keeping the entire price and detour smallest.
  • the objective function is expressed as maximizing (example): ⁇ i ⁇ U ⁇ j ⁇ C i e i , j ⁇ ⁇ 1 ⁇ i ⁇ U ⁇ j ⁇ C i e i , j ⁇ detour i , j ⁇ ⁇ 2 ⁇ i ⁇ U ⁇ i ⁇ U e i , j ⁇ pc i , j
  • ⁇ 1 and ⁇ 2 are predefined hyperparameters, that may be in the range of: 0 ⁇ ⁇ 1 ⁇ 1 max i , j detour i , j 0 ⁇ ⁇ 2 ⁇ 1 max i , j pc i , j
  • Linear constrains for optimization may include
  • the system recommends the user the charging station that intends to be optimal for price and detour while meeting all condition in the personal request.
  • Fig. 3 shows an embodiment comprising vehicle data 310, charging station data 320, pre-filtering module 330, objective function 340, constraints 350, optimization module 360, and allocation module 370.
  • Vehicle data 310 contains information regarding electrical vehicle, such as locations, battery levels, current and planned trip information, etc.
  • Charging station data 320 contains information regarding charging stations, such as locations, price schemes and prices, availability, opening hours, etc.
  • Optional pre-filter module 330 filters vehicles and /or charging stations. The pre-filter module 330 may filter vehicles that are in need of charging as described herein. Pre-filter module 330 may also reduce the number of potential charging stations for each vehicle by filter charging stations that clearly is beyond reach for each vehicle as described herein.
  • Optimization module 360 finds optimal charging stations for vehicles by optimizing the objective function 340 on the vehicle data 310 and charging station data 320 (that may have been pre-filtered).
  • the optimization module 360 may apply constraints 350 (such as linear constraints) as described herein when finding optimal charging stations for vehicles.
  • the optimal charging stations may optionally be proposed to the users/vehicles as candidates.
  • Allocated module 370 allocated the charging stations to the vehicles.
  • Fig. 4 shows charging station allocation unit 110, charging station 120, vehicle 135, charging station communication channel 220, vehicle communication channel 230.
  • Charging station allocation unit 110 may receive a request from vehicle 135 (such as via vehicle communication channel 230), to charge a vehicle.
  • the charging station allocation unit 110 may, using the data provided in the request, allocate charging station 120 to vehicle 135 using a method as described herein.
  • the charging station allocation unit 110 may also propose charging station 120 to vehicle 135 (and temporary book charging station 120), based on data provided in the request, and the vehicle may, in response, confirm the proposal.
  • the charging station 120 may be allocated to vehicle 135 on receipt of the confirmation.
  • the charging station allocation unit 110 may further, such as via charging station communication channel 220, signal the allocation of charging station 120 to charging station 120.
  • the charging station 120 may prevent other vehicles, such as vehicle 130, from using the charging station during the allocation to vehicle 135.
  • the method and/or the charging station allocation unit 110 may be implemented in a platform for allocating charging stations to vehicles.
  • the platform may allow suppliers and consumers to interact with the platform.
  • Charging station suppliers may, as examples, provide following information to platform: operation business hours, pricing policy such as charging flat fee or price per hour or price per energy unit, or earning target), etc.
  • the platform may offer the following information to the suppliers: allocation of charging station to vehicle, suggestion for optimizing the business value.
  • Consumers may provide the following information to platform: information about current location and batter level, information about next trip, selection and confirmation for recommended charging service.
  • the platform may further offer the following information to consumers: recommend the charging service, optimize consumer experience to minimize the charging cost, information for navigation to the charging station from the departure, or current location.
  • the platform is provided with valuable data on charging stations and vehicles. It may optimize the global system performance as a whole, as well as bringing the personal intelligence and relevant service for consumers.
  • the platform enables highly distributed charging services to work efficiently, including large amount of small service providers through the recommendation and price policy.

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